Interestingly, popular DA benchmarks in the computer vision community create their unlabeled data by choosing images of the \(n\)  categories and then removing the labels. This strategy is used in DomainNet [1]} which is one of the most recent benchmarks and also in VisDa-17 [2]} that is also a well-known benchmark. Even more interestingly, in VisDa-17 dataset, not only the unlabeled data (adopted from MS-COCO dataset) is from the same \(n\)  categories of interest, but also the images are cropped to contain only the bounding box of those objects of interest. Note that the same strategy is used in most self-supervised learning literature when using ImageNet dataset with no labels as the source of unlabeled data. We call this a curated (standard) unlabeled data and we call an unlabeled dataset that may contain any object category as an un-curated data. Fig. REF  shows some samples from standard and our un-curated VisDa-17 dataset for comparison.
